PrePrint: Business process analytics using a big data approach
Continuous improvement of business processes is a challenging task that requires complex and robust supporting systems. The use of advanced analytics methods and emerging technologies such as business intelligence systems, business activity monitoring, predictive analytics, behavioural pattern recognition and “what-if” type simulations are essential to assist business users in the continuous improvement of their processes. Nevertheless, the high volumes of event data produced by the execution of processes during the business lifetime prevent business users from accessing analytics data efficiently and on an acceptable response time basis. This paper presents a technological solution using a big data approach to provide business analysts with visibility on distributed process and business performance. The proposed architecture will enable end-users to analyse business performance on highly distributed environments in or near real-time.
PrePrint: XBRL in Chinese Financial Ecosystem: Diversity and Challenges
eXtensible Business Reporting Language (XBRL) is one of the most popular formats of business reports. It has been adopted by many countries and regions across the world, including China. This paper examines XBRL in China and answers the following questions: What is XBRL technology? How has it been implemented in the financial ecosystem of mainland China? What are the differences among three major Chinese XBRL taxonomies? It also highlights the challenges and opportunities of carrying out XBRL-based business analysis of the growing Big Data.
PrePrint: The Good, The Bad, and The Big: Big Data and T-Government
The big data phenomenon is pervasive throughout private and public sector domains. While profit motives make it urgent for companies in the private sector to know more about how to use big data, in the public sector there is an urgent need to study the potential of this phenomenon to improve government services. As such, one focus of this paper is to describe some drivers, barriers and best practices that affect the use of big data and associated analytics in the government domain. We present a model that illustrates how big data can result in transformational government by increased efficiency and effectiveness in the delivery of services. Our empirical basis for this model is a case vignette from the U.S. Department of Veterans Affairs while, the theoretical basis for our model is a balanced view of big data that takes into account the continuous growth and use of such data.
PrePrint: Experiencing indoor navigation on mobile devices (previously: Indoor navigation on mobile devices: problems, solutions and open issues)
Recently, indoor navigation on mobile devices has received attention from both startups and large vendors, since it has many relevant practical and commercial applications. User positioning and navigation using GPS signals is becoming more and more popular, mainly due to the increasing availability of acceptable quality sensors into low-cost consumer devices as smartphones. However, indoor GPS-navigation is highly unreliable because of the poor communication with satellites and the lack of detailed maps. In this paper we discuss the technologies allowing the indoor computation of accurate location and orientation data, as well as other issues and challenges that indoor navigation apps should cope with. In particular, we present and make explicit reference to a system for indoor navigation (running on a smartphone), which has been designed by the Authors, including the main problems that have been tackled during its implementation. The paper is and will be complemented by some explanatory videos.
PrePrint: Smart Healthcare Systems Framework: More Service Oriented, Instrumented, Interconnected and Intelligent
A sustainable healthcare information system relies on the ability to collect, process and transform healthcare data into information, knowledge and action to achieve cost-effective health outcomes on individual and population levels while meeting current consumer demands without reducing its capacity to provide services to future. Healthcare providers have many complex and unique challenges. This paper proposes a systematic framework for conceptualizing the data-driven and mobile- and cloud-enabled smart healthcare systems. With adoption of smart healthcare systems, - more service oriented, more instrumented (from sensors to smart phones for monitoring health), interconnected (local and global epidemiological patterns), and intelligent (algorithms help recognize patterns and suggest appropriate responses from lots of data) - healthcare organizations can provide cost effective quality healthcare services with less IT set-up cost and reduced risk.